Accurate graded crushing system for soybean meal and intelligent control method

Abstract

The invention discloses a soybean meal precise grading crushing system and an intelligent control method, and relates to the technical field of soybean meal precise crushing intelligent control, and the soybean meal precise grading crushing system comprises a feeding adjusting module, a frequency conversion feeding machine and a refining device are integrated, and a raw material detection sensor is configured; the pretreatment module comprises a magnetic separation impurity removal unit, a vibration screening unit and a humidity adjusting unit; the multi-stage crushing module consists of three stages of serially connected crushers, and each crusher is provided with an independent variable frequency motor; the classification screening module is provided with three layers of gradient screens and an airflow classification device; the intelligent control module is provided with a PLC and a touch screen and is internally provided with a parameter database; the material conveying module adopts closed conveying equipment; the data acquisition and feedback module integrates multiple types of sensors and transmits data to the control module. The soybean meal crushing precision and uniformity are improved through pretreatment, multi-stage crushing and intelligent grading, the operation cost is reduced through the energy consumption optimization module, the product quality is maintained through the material storage management module, and precision, intelligence and high efficiency of soybean meal crushing are achieved.

Description

Technical Field

[0001] This invention relates to the field of intelligent control technology for precise soybean meal grinding, and in particular to a precise soybean meal grading and grinding system and intelligent control method. Background Technology

[0002] Soybean meal, as an important feed and industrial raw material, directly affects the efficiency of subsequent processing and product quality through its grinding precision and grading effect. Currently, most soybean meal grinding equipment uses a single grinding structure combined with a simple screening device, lacking systematic pretreatment and precise grading design. During the raw material feeding process, soybean meal is prone to uneven feeding due to accumulation, leading to fluctuations in the grinding chamber load and affecting the consistency of the ground particle size. The pretreatment stage only uses simple impurity removal screens, which are insufficient to effectively remove metallic impurities and large particle agglomerates. Furthermore, the impact of raw material moisture content on the grinding effect is not considered; excessively high or low moisture content can lead to decreased grinding efficiency, uneven particle size distribution, and even material sticking to the walls and causing blockages.

[0003] The existing grading mechanisms of crushing systems have significant flaws. They mostly rely on a single screen for grading, with fixed screen apertures and a lack of dynamic calibration capabilities. This prevents flexible adjustments based on raw material characteristics and target particle size, resulting in low grading accuracy and severe mixing of products with different particle sizes. Crushing parameter adjustments are mostly manual, lacking intelligent dynamic adaptation mechanisms. They cannot adjust key parameters such as crusher speed and feed rate in real time based on changes in raw material bulk density and moisture content, affecting not only crushing efficiency and particle size accuracy but also causing energy waste. Furthermore, the systems lack comprehensive energy consumption optimization design, with equipment frequently operating under no-load or overload conditions, leading to high energy consumption per unit output and increased operating costs.

[0004] The equipment's operational status monitoring and fault handling capabilities are weak. Real-time monitoring of critical conditions such as motor temperature, bearing vibration, and material blockage is lacking, making it difficult to identify and warn of faults in a timely manner, easily leading to escalation and equipment damage. Storage silo management lacks effective temperature and humidity monitoring and material level control, making soybean meal products susceptible to moisture absorption, clumping, or spoilage during storage, affecting product quality. Furthermore, existing systems are mostly field-operated, lacking remote monitoring and management functions. Users cannot monitor system operation status in real time, and parameter adjustments and fault handling rely on on-site personnel, resulting in insufficient ease of operation and flexibility, making it difficult to meet the efficient management needs of modern production. Summary of the Invention

[0005] This invention proposes a precise soybean meal grading and pulverizing system and intelligent control method to solve the problems mentioned in the prior art.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a soybean meal precision grading and pulverizing system, comprising the following modules: The feeding adjustment module integrates a variable frequency feeder and a material equalization device. The variable frequency feeder controls the feeding rate through a speed-regulating motor. The equalization device uses a spiral blade structure to evenly disperse the soybean meal raw material, reducing the probability of accumulation and blockage. A raw material detection sensor is set at the feed inlet to monitor the raw material flow rate and initial physical state in real time. The pretreatment module includes a magnetic separation and impurity removal unit, a vibration screening unit, and a humidity control unit. The magnetic separation and impurity removal unit adsorbs metallic impurities in the raw material through a magnetic field. The vibration screening unit uses a double-layer screen to separate large particle clumps from non-metallic impurities. The humidity control unit adjusts the moisture content of the soybean meal raw material to the appropriate range for crushing through a spray device linked with a humidity sensor. The multi-stage crushing module consists of a primary crusher, an intermediate crusher, and a fine crusher connected in series according to the process sequence. The primary crusher adopts a hammer crushing structure, the intermediate crusher is equipped with a toothed roller grinding assembly, and the fine crusher is equipped with a rotary crushing blade. Each stage of the crusher is equipped with an independent variable frequency drive motor. The grading and screening module is equipped with three layers of grading screens and an air classifier. The screen apertures are distributed in a gradient that gradually decreases from top to bottom. The air classifier separates pulverized products of different particle sizes by adjusting the airflow speed. The grading screens and the air classifier work together to achieve a multi-level grading effect. The intelligent control module is equipped with a PLC controller and an industrial touch screen. It has a built-in crushing parameter database and a grading accuracy control algorithm. It receives sensor data from various modules and dynamically adjusts the feeding rate, crusher speed, grading screen vibration frequency and airflow speed. The material conveying module uses a closed screw conveyor and a negative pressure suction device to convey soybean meal products of different particle sizes to the corresponding storage bins according to the grading results. Material temperature sensors are installed during the conveying process to monitor changes in the physical state of the material. The data acquisition and feedback module integrates five sensors: flow rate, humidity, temperature, particle size detection, and motor power. It collects data in real time and transmits the collected data to the intelligent control module.

[0007] Furthermore, it also includes a self-adjusting module for the crushing blades. This module has a built-in blade wear detection unit and a speed compensation unit. The blade wear detection unit monitors the operating status of the blades through vibration sensors and current sensors. The speed compensation unit dynamically adjusts the speed of the crushing motor according to the degree of wear. It is also equipped with a blade replacement reminder function, which automatically triggers an early warning when the wear reaches a set threshold.

[0008] Furthermore, it also includes a grading accuracy calibration module, which integrates a laser particle size analyzer and an automatic calibration unit. The laser particle size analyzer detects the actual particle size distribution of soybean meal products at each grade in real time, and the automatic calibration unit compares the deviation between the actual particle size and the target particle size, and dynamically adjusts the vibration frequency of the grading screen and the airflow speed of the airflow grading device.

[0009] Furthermore, it also includes an energy consumption optimization module, which has a built-in motor power monitoring unit and load matching algorithm. The motor power monitoring unit collects the operating power data of each level of crusher and conveying equipment in real time. The load matching algorithm optimizes the allocation of operating parameters of each device according to the characteristics of raw materials and crushing requirements, while recording the energy consumption data of the equipment and outputting energy consumption analysis reports.

[0010] Furthermore, it also includes a fault diagnosis and protection module, which integrates equipment operation status monitoring sensors and fault analysis units. The monitoring sensors collect real-time data on motor temperature, bearing vibration, and equipment operating noise. The fault analysis unit identifies the fault type by comparing it with the threshold values ​​of normal operating parameters and triggers the corresponding protection mechanism.

[0011] Furthermore, it also includes an intelligent management module for storage silos. Each storage silo is equipped with a level sensor, a temperature sensor, and a humidity sensor to monitor the level, temperature, and humidity changes of soybean meal products in the silo in real time. The level sensor is linked to the material conveying module, which stops conveying when the level reaches the set upper limit and automatically starts the replenishment process when the lower limit is reached.

[0012] Furthermore, it also includes a remote monitoring module, which supports establishing connections with mobile terminals or monitoring centers via the Industrial Internet to transmit information in real time. Users can remotely view the system's operation status, set crushing parameters, and receive fault alarm notifications through mobile terminals, thus realizing remote operation and management.

[0013] Furthermore, an intelligent control method for a soybean meal precision grading and grinding system includes the following steps: Raw material pretreatment control: start the pretreatment module, the magnetic separation unit adsorbs metal impurities, the vibration screening unit separates large particle clumps, the humidity sensor collects the initial moisture content of the raw material, if the moisture content is higher than the suitable range, the spray device is started to replenish water, if it is lower than the suitable range, the hot air device is used to make a small adjustment to adjust the moisture content to the preset range. Feeding parameters are set by retrieving the initial feed rate reference parameters from the parameter database of the intelligent control module based on the target particle size and raw material characteristics. The variable frequency feeder and uniform feeding device are started, and the raw material detection sensor monitors the feed flow rate in real time and dynamically adjusts the speed of the speed-regulating motor. The grinding parameters are dynamically adjusted based on the raw material characteristics and target particle size requirements. The optimal operating speed of each stage of the grinder is calculated using a grinding speed optimization model. The calculation formula is as follows: ,in To achieve the optimal grinding speed, For material characteristics weighting coefficients, This refers to the bulk density of soybean meal. For the target particle size, The coefficient representing the influence of soybean meal moisture content. To adapt the weighting coefficient to energy consumption, The rated power of the pulverizing motor, This refers to the real-time power of the crushing motor. The grading and screening control starts the grading and screening module, sets the vibration frequency of the three-layer grading screen and the initial airflow velocity of the airflow grading device, and uses a laser particle size analyzer to detect the particle size distribution of the graded product in real time. It compares the deviation between the actual particle size and the target particle size and dynamically adjusts the screen vibration frequency and airflow velocity. Data acquisition and feedback optimization: The data acquisition module collects data in real time and transmits it to the intelligent control module. The control module analyzes the deviation between the data and the preset parameters. If the particle size deviation exceeds the allowable range, the speed of the crusher or the grading parameters are adjusted. If the energy consumption per unit output exceeds the preset range, the load distribution scheme is optimized to form a closed-loop control. Material conveying and storage control: The material conveying module conveys products of different particle sizes to the corresponding storage bins according to the grading results. The material level sensor monitors the material level in the bin in real time. When the upper limit is reached, the conveying of the corresponding product is stopped. The temperature and humidity sensor monitors the storage status and issues an early warning when there is an abnormality. The system operation status monitoring and fault diagnosis module monitors equipment operating parameters in real time, identifies faults, and triggers corresponding protection mechanisms. At the same time, the remote monitoring module transmits operating data and fault information synchronously.

[0014] Furthermore, it also includes a dynamic calibration step for grading accuracy. The laser particle size analyzer detects the particle size distribution of the product at each grade according to a set cycle, calculates the deviation between the actual particle size and the target particle size, and when the deviation exceeds the set threshold, the automatic calibration unit adjusts the vibration frequency and airflow speed of the grading screen, while correcting the corresponding parameters in the crushing parameter database.

[0015] Furthermore, it also includes a dynamic energy consumption optimization step. The energy consumption optimization module collects the operating power data of each level of equipment in real time, and calculates the energy consumption per unit output by combining the raw material processing volume and crushing effect. It optimizes the operating parameters of each piece of equipment through a load matching algorithm. When the characteristics of the raw material change, it automatically adjusts the matching relationship between the feeding rate and the crusher speed to reduce the energy consumption per unit output, and generates an energy consumption analysis report at the same time.

[0016] Compared with existing technologies, the beneficial effects of this invention are: In the raw material pretreatment stage, the system effectively removes metal impurities and large particle agglomerates through the synergistic effect of magnetic separation, vibration screening and humidity adjustment, and adjusts the moisture content of the raw materials to the range suitable for crushing. This improves the purity and crushing suitability of the raw materials from the source, laying a good foundation for subsequent crushing and grading, and solving the core problems of incomplete pretreatment and unsuitable raw material conditions in traditional equipment.

[0017] The design of the crushing and grading stages achieves a dual breakthrough. The multi-stage crushing module employs a combination of hammer mill, toothed roller, and high-speed rotary structures, coupled with an independent variable frequency drive motor, allowing for flexible adjustment of the rotation speed at each stage according to the target particle size. The grading and screening module, through a three-layer gradient screen linked to an airflow grading device, combined with a laser particle size analyzer and an automatic calibration unit's closed-loop control, dynamically corrects grading parameters, significantly improving grading accuracy and ensuring that the particle size of products at different stages meets preset standards. The intelligent control module, equipped with a crushing speed optimization model, achieves dynamic parameter adaptation, avoiding the subjectivity and lag of manual adjustments. Simultaneously, the energy consumption optimization module rationally allocates equipment operating parameters through a load matching algorithm, reducing idling and overload conditions, thereby reducing energy consumption per unit output while ensuring crushing effect and improving system operating economy.

[0018] The system's safety, reliability, and ease of operation are significantly enhanced. The fault diagnosis and protection module monitors equipment operating status in real time, quickly identifies various faults, and triggers corresponding protection mechanisms to curb the escalation of faults and extend equipment lifespan. The intelligent storage silo management module achieves precise control of material conveying and storage through temperature, humidity, and material level monitoring, reducing the risk of product moisture absorption, clumping, and spoilage, and ensuring product storage quality. The remote monitoring module supports industrial internet and mobile terminal connectivity, allowing users to view operating data in real time, remotely adjust parameters, and receive fault alarms, breaking the limitations of on-site operation and improving management flexibility. Overall, this invention constructs an intelligent system integrating pretreatment, crushing, grading, conveying, and storage, achieving precise grading, dynamic control, and efficient operation of soybean meal crushing. It provides reliable technical support for improving the quality and efficiency of the soybean meal processing industry and has significant practical value and promotional significance. Attached Figure Description

[0019] Figure 1 This is a schematic block diagram of the soybean meal precision grading and pulverizing system proposed in this invention; Figure 2 A schematic block diagram of an intelligent control method for precise grading and grinding of soybean meal; Figure 3 A comparison chart of grading accuracy for different particle size ranges; Figure 4 A comparison chart of energy consumption per unit output under different processing capacities; Figure 5 A comparison chart showing the particle size uniformity before and after optimizing the crushing speed. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," and "counterclockwise," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0022] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of the stated features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified. Furthermore, the terms "installed," "connected," and "linked" should be interpreted broadly; for example, they may refer to a fixed connection, a detachable connection, or an integral connection; they may refer to a mechanical connection or an electrical connection; they may refer to a direct connection or an indirect connection through an intermediate medium; and they may refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances. The invention will now be described in further detail with reference to the accompanying drawings.

[0023] Reference Figures 1 to 5 A precision grading and grinding system for soybean meal, comprising the following modules: The feeding adjustment module integrates a variable frequency feeder and a material equalization device. The variable frequency feeder controls the feeding rate through a speed-regulating motor. The equalization device uses a spiral blade structure to evenly disperse the soybean meal raw material, reducing the probability of accumulation and blockage. A raw material detection sensor is set at the feed inlet to monitor the raw material flow rate and initial physical state in real time. The pretreatment module includes a magnetic separation and impurity removal unit, a vibration screening unit, and a humidity control unit. The magnetic separation and impurity removal unit adsorbs metallic impurities in the raw materials through a high-intensity magnetic field. The vibration screening unit uses a double-layer screen to separate large particle clumps from non-metallic impurities. The humidity control unit adjusts the moisture content of the soybean meal raw materials to the appropriate range for crushing through a spray device linked with a humidity sensor. The multi-stage crushing module consists of a primary crusher, a secondary crusher, and a fine crusher connected in series according to the process sequence. The primary crusher adopts a hammer crushing structure, the secondary crusher is equipped with a toothed roller grinding assembly, and the fine crusher is equipped with high-speed rotating crushing blades. Each stage of the crusher is equipped with an independent variable frequency drive motor, which supports independent speed adjustment. The grading and screening module is equipped with three layers of grading screens and an airflow grading device. The screen apertures are distributed in a gradient that gradually decreases from top to bottom. The airflow grading device separates pulverized products of different particle sizes by adjusting the airflow speed. The grading screens and the airflow grading device work together to achieve a multi-level precise grading effect. The intelligent control module is equipped with a PLC controller and an industrial touch screen. It has a built-in crushing parameter database and a grading accuracy control algorithm. It receives sensor data from various modules and dynamically adjusts the feeding rate, crusher speed, grading screen vibration frequency and airflow speed. The material conveying module uses a closed screw conveyor and a negative pressure suction device to convey soybean meal products of different particle sizes to the corresponding storage bins according to the grading results. Material temperature sensors are installed during the conveying process to monitor changes in the physical state of the material. The data acquisition and feedback module integrates a flow sensor, humidity sensor, temperature sensor, particle size detection sensor, and motor power sensor. It collects data such as raw material flow rate, moisture content, crushing temperature, product particle size, and motor operating power in real time, and transmits the collected data to the intelligent control module to provide data basis for dynamic parameter adjustment.

[0024] The invention also includes a self-adjusting module for crushing blades. This module has a built-in blade wear detection unit and a speed compensation unit. The blade wear detection unit monitors the blade operating status through vibration sensors and current sensors. The speed compensation unit dynamically adjusts the speed of the crushing motor according to the degree of wear. It is also equipped with a blade replacement reminder function. When the wear reaches a set threshold, an early warning prompt is automatically triggered to maintain the stability of crushing efficiency and particle size accuracy.

[0025] This invention also includes a grading accuracy calibration module, which integrates a laser particle size analyzer and an automatic calibration unit. The laser particle size analyzer detects the actual particle size distribution of soybean meal products at each grade in real time. The automatic calibration unit compares the deviation between the actual particle size and the target particle size and dynamically adjusts the vibration frequency of the grading screen and the airflow speed of the airflow grading device. The grading accuracy is improved through closed-loop control, so that the particle size of products at different grades meets the preset standard.

[0026] This invention also includes an energy consumption optimization module, which has a built-in motor power monitoring unit and a load matching algorithm. The motor power monitoring unit collects the operating power data of each level of crusher and conveying equipment in real time. The load matching algorithm optimizes the allocation of operating parameters of each device according to the characteristics of the raw materials and the crushing requirements, reduces the no-load or overload conditions of the equipment, maximizes the reduction of system energy consumption while ensuring the crushing effect, and records the energy consumption data of the equipment and outputs energy consumption analysis reports.

[0027] This invention also includes a fault diagnosis and protection module, which integrates equipment operation status monitoring sensors and a fault analysis unit. The monitoring sensors collect data such as motor temperature, bearing vibration, and equipment operating noise in real time. The fault analysis unit identifies fault types such as motor overload, bearing wear, and material blockage by comparing them with normal operating parameter thresholds, and triggers corresponding protection mechanisms, such as automatic shutdown, feed cut-off, and alarm prompts, to prevent equipment damage caused by the expansion of faults.

[0028] This invention also includes an intelligent management module for storage silos. Each storage silo is equipped with a level sensor, a temperature sensor, and a humidity sensor to monitor the level, temperature, and humidity changes of soybean meal products in the silo in real time. The level sensor is linked to the material conveying module. When the level reaches the upper limit, the conveying stops, and when the lower limit is reached, the replenishment process is automatically started. At the same time, the temperature and humidity sensors monitor the storage status of the product to reduce the risk of moisture absorption, clumping, and spoilage.

[0029] This invention also includes a remote monitoring module, which supports establishing a connection with a mobile terminal or monitoring center through the Industrial Internet to transmit information such as system operating parameters, equipment status, and product quality data in real time. Users can remotely view the system operation status, set crushing parameters, and receive fault alarm notifications through mobile terminals, thereby realizing remote operation and management and enhancing the convenience of system operation and application flexibility.

[0030] This invention includes the following steps: Raw material pretreatment control: start the pretreatment module, the magnetic separation unit adsorbs metal impurities, the vibration screening unit separates large particle clumps, the humidity sensor collects the initial moisture content of the raw material, if the moisture content is higher than the suitable range, the spray device is started to replenish water, if it is lower than the suitable range, the hot air device is used to make a small adjustment to adjust the moisture content to the preset range. Feeding parameters are set according to the target particle size and raw material characteristics. The initial feed rate reference parameters are retrieved from the parameter database of the intelligent control module, and the variable frequency feeder and uniform feeding device are started. The raw material detection sensor monitors the feed flow rate in real time and dynamically adjusts the speed of the speed-regulating motor to maintain the uniformity and stability of the feed. The grinding parameters are dynamically adjusted based on the raw material characteristics and target particle size requirements. The optimal operating speed of each stage of the grinder is calculated using a grinding speed optimization model. The calculation formula is as follows: ,in To achieve the optimal grinding speed, For material characteristics weighting coefficients, This refers to the bulk density of soybean meal. For the target particle size, The coefficient representing the influence of soybean meal moisture content. To adapt the weighting coefficient to energy consumption, The rated power of the pulverizing motor, To determine the real-time power of the crushing motor, the intelligent control module adjusts the speed of the variable frequency drive motors of each stage of the crusher based on the calculation results; The grading and screening control starts the grading and screening module, sets the vibration frequency of the three-layer grading screen and the initial airflow velocity of the airflow grading device, and uses a laser particle size analyzer to detect the particle size distribution of the graded products in real time. By comparing the deviation between the actual particle size and the target particle size, the vibration frequency of the screen and the airflow velocity are dynamically adjusted to achieve the precise grading target. Data acquisition and feedback optimization: The data acquisition module collects data such as raw material flow rate, moisture content, crushing temperature, product particle size, and motor operating power in real time and transmits them to the intelligent control module. The control module analyzes the deviation between the data and the preset parameters. If the particle size deviation exceeds the allowable range, the crusher speed or grading parameters are adjusted. If the energy consumption per unit output exceeds the preset range, the load distribution scheme is optimized to form a closed-loop control. Material conveying and storage control: The material conveying module conveys products of different particle sizes to the corresponding storage bins according to the grading results. The material level sensor monitors the material level in the bin in real time. When the upper limit is reached, the conveying of the corresponding product is stopped. The temperature and humidity sensor monitors the storage status and issues an early warning when there is an abnormality. The system operation status monitoring and fault diagnosis module monitors equipment operating parameters in real time, identifies faults such as motor overload, bearing wear, and material blockage, and triggers corresponding protection mechanisms. At the same time, the remote monitoring module transmits operating data and fault information synchronously, allowing users to keep track of the system status in real time.

[0031] This invention also includes a dynamic calibration step for grading accuracy. The laser particle size analyzer detects the particle size distribution of the product at each grade according to a set cycle, calculates the deviation between the actual particle size and the target particle size, and when the deviation exceeds the set threshold, the automatic calibration unit adjusts the vibration frequency and airflow speed of the grading screen, and corrects the corresponding parameters in the crushing parameter database. Through multiple iterative calibrations, the long-term stability of grading accuracy is enhanced, so that the product particle size meets the preset standard requirements during long-term operation.

[0032] This invention also includes a dynamic energy consumption optimization step. The energy consumption optimization module collects the operating power data of each level of equipment in real time, and calculates the energy consumption per unit output by combining the raw material processing volume and crushing effect. The operating parameters of each equipment are optimized through a load matching algorithm. When the characteristics of the raw material change, the matching relationship between the feeding rate and the crusher speed is automatically adjusted to reduce the idle or overload running time of the equipment. Under the premise of ensuring crushing efficiency and grading accuracy, the energy consumption per unit output is minimized. At the same time, an energy consumption analysis report is generated to provide data support for subsequent parameter optimization.

[0033] The following two examples further illustrate specific embodiments of the present invention: Example 1: Application of Precision Grading and Grinding of Feed-Grade Soybean Meal This embodiment addresses the diverse needs of the feed processing industry for soybean meal particle size by employing a precise soybean meal grading and grinding system and intelligent control method. This system enables precise grading and grinding of soybean meal at three particle size levels: 2mm, 1mm, and 0.5mm, with a processing capacity of 5 tons per hour.

[0034] After system startup, the variable frequency feeder of the feeding adjustment module and the spiral blade uniform feeder operate in conjunction. The raw material detection sensor monitors the soybean meal raw material flow rate in real time, and the intelligent control module dynamically adjusts the speed of the speed-regulating motor according to the preset processing capacity to stably control the feeding rate within the corresponding range. The raw material enters the pretreatment module, where the magnetic separation and impurity removal unit adsorbs metal impurities such as iron filings mixed in the raw material through a high-intensity magnetic field. The vibrating screening unit with a double-layer screen structure separates large particle clumps with a diameter greater than 5mm, and the humidity sensor collects the initial moisture content of the raw material. When the detected moisture content is lower than the suitable range for crushing, the hot air device is activated for fine adjustment; when the moisture content is higher than the suitable range, the spray device is activated to replenish water, ultimately adjusting the moisture content of the raw material to the preset range.

[0035] The pre-treated raw materials enter a multi-stage crushing module. The primary crusher uses a hammer mill to crush the raw materials to a diameter of less than 3mm. The intermediate crusher further grinds the raw materials to a diameter of less than 1.5mm using a toothed roller grinding assembly. The fine crusher uses high-speed rotating grinding blades to complete the fine crushing. The intelligent control module calculates the optimal operating speed of each stage of the crusher based on the raw material bulk density and target particle size parameters using a crushing speed optimization model. The calculation formula is as follows: Set material characteristic weighting coefficients. The energy consumption adaptation weighting coefficient is 0.6. The bulk density of soybean meal is 0.4. 0.6 g / cm 3 Target particle size The thickness is 0.5 mm, and the influence coefficient of moisture content is... The value is 0.8, and the rated power of the crushing motor is... 30kW, real-time power It is 22kW. Substituting into the formula, we can get... The intelligent control module adjusts the speed of the fine pulverizer motor accordingly.

[0036] The pulverized material enters the grading and screening module. Three layers of grading screens have apertures distributed in a gradient from top to bottom: 2mm, 1mm, and 0.5mm. An airflow grading device adjusts the airflow speed to assist in separating products of different particle sizes. A laser particle size analyzer monitors the particle size distribution of the products at each grade in real time. An automatic calibration unit compares the actual particle size with the target particle size deviation and dynamically adjusts the screen vibration frequency and airflow speed. The data acquisition and feedback module integrates various sensors to collect data such as raw material flow rate, moisture content, pulverizing temperature, product particle size, and motor power in real time and transmits this data to the intelligent control module, forming a closed-loop control system.

[0037] The material conveying module employs a closed screw conveyor and a negative pressure suction device to transport products of different particle sizes to their corresponding storage bins. The intelligent management module for the storage bins uses level sensors, temperature sensors, and humidity sensors to monitor the bin's condition in real time. Conveying stops when the material level reaches the upper limit and replenishment begins when it reaches the lower limit. The fault diagnosis and protection module monitors parameters such as motor temperature and bearing vibration, identifies faults, and triggers protection mechanisms. The remote monitoring module synchronously transmits operational data to the monitoring center, supporting remote viewing and parameter adjustment by users.

[0038] Table 1 Comparison of Grinding and Grading Accuracy in Example 1

[0039] Table 1 clearly demonstrates the significant advantages of this system in terms of grading accuracy. Traditional grinding systems rely on a single screen for grading, lacking a dynamic calibration mechanism. Grading accuracy at each level is generally below 85%, with the 0.5mm level achieving only 72%. This results in severe mixing of products of different particle sizes, failing to meet the precise ingredient requirements of feed processing. This system, through the linkage of a three-layer gradient screen and an airflow grading device, combined with real-time detection by a laser particle size analyzer and closed-loop control by an automatic calibration unit, dynamically adjusts grading parameters. Grading accuracy at each level is improved to over 97%, with the 0.5mm level reaching 99%. This effect stems from the synergistic optimization of the grading mechanism and intelligent control, ensuring that the product particle size strictly conforms to preset standards, providing high-quality raw materials for feed processing and addressing the industry pain point of insufficient grading accuracy in traditional systems.

[0040] Example 2: Application of industrial-grade soybean meal intensive processing and grinding This embodiment addresses the need for ultra-fine particle size in the intensive processing of industrial-grade soybean meal. It employs a precise soybean meal grading and pulverizing system and intelligent control method to achieve precise grading and pulverizing at three particle size levels: 1mm, 0.3mm, and 0.1mm, with a processing capacity of 3 tons per hour.

[0041] After the system starts up, the variable frequency feeder of the feed adjustment module adjusts the speed of the speed-regulating motor to control the feeding rate according to the characteristics of industrial-grade soybean meal raw materials. The spiral blades of the uniform feeding device evenly disperse the raw materials, reducing load fluctuations in the grinding chamber. The raw materials enter the pretreatment module, where the magnetic separation unit adsorbs metal impurities, the vibration screening unit separates large particle clumps, the humidity sensor collects the initial moisture content, and the humidity adjustment unit adjusts the moisture content to the suitable range for industrial-grade soybean meal grinding through a spray or hot air device.

[0042] The pre-treated raw materials enter a multi-stage grinding module. The primary grinder's hammer mill crushes the material to a diameter of less than 2mm, the intermediate grinder's toothed roller mill grinds it to a diameter of less than 0.8mm, and the fine grinder's high-speed rotating blades complete the ultrafine grinding. The intelligent control module, based on the bulk density of industrial-grade soybean meal and the target ultrafine particle size parameters, calculates the optimal grinding speed using a grinding speed optimization model. The calculation formula is as follows: Set material characteristic weighting coefficients. The energy consumption adaptation weighting coefficient is 0.7. The value is 0.3, and the bulk density ρ of soybean meal is 0.55 g / cm³. 3 Target particle size The thickness is 0.1 mm, and the influence coefficient of moisture content is... The value is 0.75, and the rated power of the crushing motor is... 37kW, real-time power It is 28kW. Substituting into the formula, we can get... =0.7×(0.55×0.1) / 0.75+0.3×(37-28) / 37=0.7×0.073+0.3×0.243≈0.051+0.073=0.124×1000=1240r / min. The intelligent control module adjusts the speed of the fine pulverizer motor accordingly to meet the ultrafine pulverization requirements.

[0043] The pulverized material enters the grading and screening module. Three layers of grading screens have apertures gradients of 1mm, 0.3mm, and 0.1mm. An airflow grading device increases airflow velocity to enhance the separation of ultrafine particles. A laser particle size analyzer accurately detects the particle size of the product at each level. An automatic calibration unit adjusts the screen vibration frequency and airflow velocity based on deviations to ensure grading accuracy. A data acquisition and feedback module collects various operational data in real time, and an intelligent control module dynamically optimizes pulverization and grading parameters, forming a closed-loop control system.

[0044] The negative pressure suction device in the material conveying module prevents ultrafine soybean meal particles from flying during the conveying process, and delivers products of different particle sizes to the corresponding storage bins. The intelligent management module of the storage bins monitors the material level, temperature and humidity to prevent ultrafine soybean meal from getting damp and clumping. The fault diagnosis and protection module monitors the equipment status in real time, and the remote monitoring module supports remote operation and management by users.

[0045] Table 2 Comparison of Energy Consumption per Unit Output in Example 2

[0046] Table 2 clearly demonstrates the outstanding energy consumption optimization effect of this system. Traditional grinding systems often force processing by increasing motor speed when grinding ultrafine particles, leading to overload conditions and a significant increase in energy consumption per unit output. For example, the energy consumption at the 0.1mm setting reaches as high as 180 kW·h / t, resulting in high operating costs. This system calculates optimal operating parameters through a grinding speed optimization model and combines this with a load matching algorithm in the energy consumption optimization module to rationally allocate the operating power of each grinding stage, avoiding equipment overload. Simultaneously, the closed-loop control mechanism dynamically adjusts parameters based on raw material characteristics, significantly reducing energy consumption per unit output at each setting. The energy consumption at the 0.1mm setting is only 98 kW·h / t, a reduction of more than 45% compared to traditional systems. This effect stems from the synergistic effect of intelligent control and energy consumption optimization, which significantly reduces operating costs and improves the economic benefits of industrial-grade soybean meal deep processing while ensuring ultrafine grinding results.

[0047] Reference Figure 3 This diagram visually demonstrates the core advantage of this invention in terms of grading accuracy. Traditional grinding systems rely on fixed-aperture screens for grading, lacking a dynamic calibration mechanism. The smaller the particle size, the lower the grading accuracy; the 0.1mm level only achieves 68%, failing to meet the demands of fine processing. This invention uses a three-layer gradient screen linked to an airflow grading device, combined with a laser particle size analyzer to detect particle size distribution in real time. An automatic calibration unit dynamically adjusts the screen vibration frequency and airflow speed based on deviations, forming a closed-loop control. Whether for ultrafine particles of 0.1mm or conventional particles of 2mm, the grading accuracy remains consistently above 97%, reaching 99% for the 0.1mm and 0.5mm levels. This achievement solves the industry pain point of traditional systems where grading accuracy significantly decreases with decreasing particle size, ensuring that the particle size of soybean meal products at different levels strictly conforms to preset standards, adapting to the precise material requirements of different scenarios such as feed and industrial processing.

[0048] Reference Figure 4The figure clearly illustrates the energy consumption optimization effect of this invention. Traditional grinding systems do not dynamically adjust operating parameters according to the throughput. When the throughput increases, the motor is prone to overload, and the unit energy consumption rises rapidly, reaching 125 kW·h / t at a throughput of 5 t / h. This invention calculates the optimal operating speed through a grinding speed optimization model and combines it with the load matching algorithm of the energy consumption optimization module to rationally allocate the power of each grinding mill according to the throughput and raw material characteristics, avoiding idling or overload operation. Even when the throughput increases from 1 t / h to 5 t / h, the unit energy consumption only increases slightly from 42 kW·h / t to 52 kW·h / t, and the overall energy consumption level is reduced by more than 40% compared to the traditional system. This advantage significantly reduces the operating cost of soybean meal grinding and processing, and is especially suitable for large-scale production scenarios, improving the economy and sustainability of the process.

[0049] Reference Figure 5 This figure highlights the core value of the grinding speed optimization model of this invention. Traditional grinding systems have a fixed grinding speed, which cannot adapt to changes in raw material moisture content, resulting in large fluctuations in particle size uniformity and an overall low uniformity (only 65% ​​at 16% moisture content), and high particle size dispersion in the product. This invention, through a grinding speed optimization model, calculates the optimal grinding speed by combining parameters such as raw material moisture content and bulk density. Even when the moisture content varies between 8% and 16%, the particle size uniformity remains stable above 95%. At a moisture content of 12%, the uniformity reaches 98%, fully demonstrating the model's dynamic adaptability to raw material characteristics. This effect solves the problem of unstable grinding results caused by changes in raw material state in traditional systems, ensuring that soybean meal products with high uniformity can be produced from raw materials with different moisture contents, thus improving the consistency of product quality.

[0050] The above are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

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2. The precise grading and pulverizing system of soybean meal according to claim 1, characterized in that, The application relates to a bean dreg crushing and grading system.

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The fault analysis unit compares the data with normal running parameter threshold, identifies fault type and triggers corresponding protection mechanism.

6. The precise grading and pulverizing system of soybean meal according to claim 1, characterized in that, It also includes a smart management module for storage silos. Each storage silo is equipped with a level sensor, a temperature sensor and a humidity sensor to monitor the level, temperature and humidity of the soybean meal in the silo in real time. The level sensor is connected to the material conveying module. When the level reaches the upper limit, the conveying stops. When the level reaches the lower limit, the replenishment process is automatically started.

7. The precise grading and pulverizing system of soybean meal according to claim 1, characterized in that, It also includes a remote monitoring module that supports connection with mobile terminals or monitoring centers through industrial internet, real-time transmission of information, remote viewing of system operation by users through mobile terminals, setting of crushing parameters, receiving of fault alarm notifications, and remote operation and management.

8. The intelligent control method of the precise grading and crushing system of soybean meal according to any one of claims 1-7, characterized in that, The method comprises the following steps: Raw material pretreatment control: start the pretreatment module, adsorb metal impurities with the magnetic separation unit, separate large particle agglomerates with the vibrating screen unit, collect the initial moisture content of the raw material with the humidity sensor, and start the spray device to add water if the moisture content is higher than the adaptive range or adjust the moisture content with the hot air device if the moisture content is lower than the adaptive range to adjust the moisture content to the preset range; Feeding parameter setting: according to the target crushing particle size and the characteristics of the raw material, retrieve the initial feeding rate reference parameter from the parameter database of the intelligent control module, start the variable frequency feeder and the uniform material device, and dynamically adjust the speed of the motor with the real-time monitoring of the feeding flow by the raw material detection sensor; The crushing parameter dynamic adjustment is based on the material characteristics and target particle size requirement, and the optimal running speed of each stage of the crusher is calculated through a crushing speed optimization model, and the calculation formula is wherein is the optimal crushing speed, is the material characteristic weight coefficient, is the soybean meal bulk density, is the target crushing particle size, is the soybean meal moisture content influence coefficient, is the energy consumption adaptation weight coefficient, is the rated power of the crushing motor, is the real-time power of the crushing motor; Classification control: start the classification module, set the vibration frequency of the three-layer classification screen and the initial air flow velocity of the air classification device, and dynamically adjust the screen vibration frequency and the air flow velocity according to the real-time detection of the particle size distribution of the classified product by the laser particle size analyzer and the comparison of the deviation between the actual particle size and the target particle size; Data acquisition and feedback optimization: the data acquisition module collects data in real time, which is transmitted to the intelligent control module. The control module analyzes the deviation between the data and the preset parameters. If the particle size deviation exceeds the allowed range, adjust the speed of the crusher or the classification parameters. If the energy consumption per unit output exceeds the preset range, optimize the load distribution scheme to form a closed-loop control; Material conveying and storage control: the material conveying module conveys the products of different particle sizes to the corresponding storage silos according to the classification results. The level sensor monitors the level in the silo in real time. When the level reaches the upper limit, the conveying of the corresponding product stops. The temperature and humidity sensors monitor the storage state and issue a warning when there is an abnormality. System running state monitoring: the fault diagnosis module monitors the running parameters of the device in real time, identifies faults, triggers corresponding protection mechanisms, and synchronously transmits running data and fault information through the remote monitoring module. 9.The intelligent control method of the soybean meal precision grading and pulverizing system according to claim 8, characterized in that, It also includes a dynamic calibration step for classification accuracy. The laser particle size analyzer detects the particle size distribution of each grade product at a set period. When the deviation value exceeds the set threshold, the automatic calibration unit adjusts the vibration frequency of the classification screen and the air flow velocity, and simultaneously corrects the corresponding parameters in the crushing parameter database. 10.The intelligent control method of the soybean meal precision grading and pulverizing system according to claim 8, characterized in that, It also includes the energy consumption dynamic optimization step, the energy consumption optimization module collects the running power data of each level device in real time, combines the raw material processing capacity and the crushing effect, calculates the energy consumption per unit output, optimizes the running parameters of each device through the load matching algorithm, automatically adjusts the matching relationship of the feeding rate and the speed of the crusher when the characteristics of the raw material change, reduces the energy consumption level per unit output, and generates an energy consumption analysis report.

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